Artificial Intelligence, Income Distribution and Economic Growth

Thomas Gries and Wim Naudé

VfS Annual Conference 2020 (Virtual Conference): Gender Economics from Verein für Socialpolitik / German Economic Association

Abstract: The economic impact of Artificial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard represen- tative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggre- gate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply-side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.

Keywords: Technology; artificial intelligence; productivity; labor demand; income distribution; growth theory (search for similar items in EconPapers)
JEL-codes: E21 E25 J24 O33 O47 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-fdg, nep-gro, nep-ict and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Artificial Intelligence, Income Distribution and Economic Growth (2020)
Working Paper: Artificial Intelligence, Income Distribution and Economic Growth (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc20:224623

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